Balancing inverted pendulum using reinforcement algorithms

With the advancements in technology, robots has become systems that can learn and achieve complex behaviors in real life with the help of machine learning algorithms. Among those algorithms, reinforcement learning algorithms are widely used in robotics to teach the systems by trials and errors. In this work, our goal is to use the two different reinforcement algorithms, Q-learning and Adaptive Heuristic Critic (AHC) algorithm, on well-known cart-pole balancing problem and examine the performance results. We used Box2d physics engine simulator to simulate the cartpole model and the environment. Observing the experimental results, AHC algorithm was able to balance the system for more step counts than Q-learning algorithm.